Bayesian modeling of virus removal efficiency in wastewater treatment processes

T. Ito, T. Kato, K. Takagishi, S. Okabe, D. Sano

研究成果: Article査読

10 被引用数 (Scopus)

抄録

Left-censored datasets of virus density in wastewater samples make it difficult to evaluate the virus removal efficiency in wastewater treatment processes. In the present study, we modeled the probabilistic distribution of virus removal efficiency in a wastewater treatment process with a Bayesian approach, and investigated how many detect samples in influent and effluent are necessary for accurate estimation. One hundred left-censored data of virus density in wastewater (influent and effluent) were artificially generated based on assumed log-normal distributions and the posterior predictive distribution of virus density, and the log-ratio distribution were estimated. The estimation accuracy of distributions was quantified by Bhattacharyya coefficient. When it is assumed that the accurate estimation of posterior predictive distributions is possible when a 100% positive rate is obtained for 12 pairs of influent and effluent, 11 out of 144, 60 out of 324, and 201 out of 576 combinations of detect samples gave an accurate estimation at the significant level of 0.01 in a Kruskal-Wallis test when the total sample number was 12, 18, and 24, respectively. The combinations with the minimum number of detect samples were (12, 9), (16, 10), and (21, 8) when the total sample number was 12, 18, and 24, respectively.

本文言語English
ページ(範囲)1789-1795
ページ数7
ジャーナルWater Science and Technology
72
10
DOI
出版ステータスPublished - 2015 11月
外部発表はい

ASJC Scopus subject areas

  • 環境工学
  • 水の科学と技術

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